Generally, in an image or a letter recognition system, analysis of image information is performed by classification or discrimination of an image signal inputted from an image input system, such as a camera or an image scanner. However, the image signal inputted from the image input system is frequently accompanied by distortion or noise.
It is difficult to classify or discern an image which has been degraded by distortion or noise. Thus, it is much more effective if analysis of the image information is performed after improving the degraded image.
A LPF, an HPF, an average filter, etc., can be used to improve the degraded image, but all of these filters have a disadvantage in that an edge of the image information can be easily distorted or lost. Therefore, rank filters are widely used as a means for improving image information while at the same time maintaining the edge information.
Such rank filters have been described in the following papers: [I] R. M. Hodgson, D. G. Bailey, M. J. Naylor, A. L. M. Ng and S. J. Mcneill, Properties, implementations and applications of rank filters, image and vision computing, Vol. 3, No. 1, Feb. 1985., [II] Ho-Ming Lin, Alan N. Willson, Median Filtering with Adaptive Length, IEEE Transactions on circuits and systems, Vol. 35, No. 6, June 1988., [III] J. Patrick Fitch, Edward J. Coyle, and C. Gallagher, JR, Median Filtering By Threshold Decomposition, IEEE ASSP, Vol. ASSP-32, No. 6, 1984., [IV] Kemal Oflazer, Design and Implementation of Single chip 1-D Median Filter, IEEE ASSP, Vol. ASSP-31, No. 5, 1983.
However, conventional rank filters implemented by using general logic circuits have low processing speed, a large volume and a complex circuit arrangement.